1 About

This data notebook is based on a model presented at the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

If you want to cite the method/model please use:

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at the International Conference on Evolving Cities, MAST Mayflower Studios, Southampton, United Kingdom. 22 - 24 Sep 2021.

2 Citing this data notebook

If you wish to re-use material from this data notebook please cite it as:

Ben Anderson (2021) Data notebook: Simulating a local emissions levy to fund local energy effiency retrofit: New Forest. University of Southampton, United Kingdom

License: CC-BY

Share, adapt, give attribution.

3 Highlights

This data notebook estimates the value of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. These emissions are all consumption, gas and electricity. It does this under two scenarios - a simple carbon value multiplier and a rising block tariff.

It then compares these with estimates of the cost of retrofitting EPC band dwellings D-E and F-G in each LSOA and for the whole area under study.

Key results:

Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e - which areas emit the most?
Table 4.6 shows total levy generated under Scenario 1
Table 4.12 shows total levy generated under Scenario 2
Figure 4.16 compares the scenarios in terms of % of levy generated by areas in each IMD decile while Figure 4.17 compares the levy generated under each scenario at LSOA level. In both cases, Scenario 2 should be lower in more deprived areas and higher in less deprived areas.
Table 4.13 shows total retrofit costs and Figure 4.19 shows the LSOA level retrofit costs per dwelling by IMD decile for comparison with Figure 4.1
Figure 4.20 shows the years to pay back under Scenario 1 for an all emissions levy while * Figure 4.24 does the same for Scenario 2
Figure 4.22 shows what would happen after year 1 if the levy were shared equally across LSOAs (all emissions, Secenario 1) and Figure 4.26 shows the same for Scenario 2.
Figure 4.28 shows payback years under each Scenario assuming a constant all emissions levy

4 Emissions Levy Case Study: New Forest

This data notebook estimates a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator.

The model applies carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. It then sums these values to given an overall levy revenue estimate for the area in the case study.

The data notebook then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.

Finally the data notebook compares the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so the data notebook also analyses the extent to which redistribution of revenue from high emissions areas (households) would be required.

It should be noted that this area level analysis uses mean emissions per household. It will therefore not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy values that might be expected.

NB: no maps in the interests of speed

4.1 Data

The model uses a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions.

CREDS place-based emissions estimates (contains data from a wide range of sources including https://epc.opendatacommunities.org/ for EPCs)
2018 residential gas & electricity use data
Indices of Deprivation 2019
Urban/Rural classification at LSOA level
various LSOA look-up tables from geoportal

All analysis is at LSOA level. Cautions on inference from area level data apply.

4.2 LSOA level emissions estimates

See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/

“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”

“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."

Source: https://www.carbon.place/

Notes:

Emissions are presented as per capita…
Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc

Table 4.1: Summary of CREDS per capita data for LSOAs: New Forest
region	nLSOAs	mean_KgCo2ePerCap	sd_KgCo2ePerCap
South East	113	9,473.6	2,934.4

Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings

Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)

First check the n electricity meters logic…

##           LSOA11NM         WD18NM nGasMeters nElecMeters epc_total
## 1: New Forest 023C        Milford        985        1082       571
## 2: New Forest 017D Lymington Town        976        1311       943
## 3: New Forest 022D         Becton        955         980       547
## 4: New Forest 022A         Barton        939         955       491
## 5: New Forest 022B         Barton        939        1088       621
## 6: New Forest 023D        Milford        919        1075       635

##           LSOA11NM                             WD18NM nGasMeters nElecMeters epc_total
## 1: New Forest 017D                     Lymington Town        976        1311       943
## 2: New Forest 012D                     Ringwood South        798        1190       799
## 3: New Forest 017B Brockenhurst and Forest South East         19        1124       636
## 4: New Forest 022B                             Barton        939        1088       621
## 5: New Forest 023C                            Milford        985        1082       571
## 6: New Forest 023D                            Milford        919        1075       635

(#tab:check_nElecMeters)LSOAs with highest number of gas meters (after cleaning)
LSOA11NM	WD18NM	nGasMeters	nElecMeters	epc_total
New Forest 023C	Milford	985	1,082	571
New Forest 017D	Lymington Town	976	1,311	943
New Forest 022D	Becton	955	980	547
New Forest 022A	Barton	939	955	491
New Forest 022B	Barton	939	1,088	621
New Forest 023D	Milford	919	1,075	635

Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.

There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.

Check that the assumption seems sensible…

Check for outliers - what might this indicate?

4.2.1 Estimate per dwelling emissions

We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.

## # Summary of per dwelling values

Table 4.2: Data summary
Name	…[]
Number of rows	113
Number of columns	9
Key	NULL
_______________________
Column type frequency:
numeric	9
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
CREDStotal_kgco2e_pdw	1	21351.02	7408.38	8483.75	16380.18	20446.68	26119.40	44440.94	▅▇▅▂▁
CREDSgas_kgco2e2018_pdw	1	2321.89	648.57	55.29	2006.21	2332.00	2695.73	3999.22	▁▁▇▆▁
CREDSelec_kgco2e2018_pdw	1	1123.63	265.10	815.99	976.89	1050.86	1178.91	2137.96	▇▃▁▁▁
CREDSmeasuredHomeEnergy_kgco2e2018_pdw	1	3445.52	638.68	1982.40	3055.25	3397.32	3850.35	5652.08	▂▇▆▂▁
CREDSotherEnergy_kgco2e2011_pdw	1	212.26	406.59	7.26	35.61	61.51	129.83	1925.98	▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2018_pdw	1	3657.78	691.31	2453.62	3151.27	3569.78	4034.92	6021.95	▆▇▅▂▁
CREDScar_kgco2e2018_pdw	1	3080.05	799.14	1234.35	2469.14	3105.21	3643.56	4945.85	▂▇▇▇▂
CREDSvan_kgco2e2018_pdw	1	458.41	410.19	67.79	252.09	315.46	485.24	2773.41	▇▂▁▁▁
CREDSpersonalTransport_kgco2e2018_pdw	1	3538.46	1010.41	1564.27	2751.60	3487.17	4113.18	7148.68	▅▇▅▂▁

Examine patterns of per dwelling emissions for sense.

4.2.1.1 All emissions

Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.

## Per dwelling T CO2e - all emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.1: Scatter of LSOA level all consumption emissions per dwelling against IMD score

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -3.9755, df = 111, p-value = 0.0001253
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5048469 -0.1800547
## sample estimates:
##        cor 
## -0.3530418

## Total emissions per dwelling (LSOA level) summary

##    LSOA11CD            WD18NM                         IMD_Decile_label All_Tco2e_per_dw
##  Length:113         Length:113         10 (10% least deprived):23      Min.   : 8.484  
##  Class :character   Class :character   8                      :21      1st Qu.:16.380  
##  Mode  :character   Mode  :character   6                      :16      Median :20.447  
##                                        9                      :14      Mean   :21.351  
##                                        7                      :13      3rd Qu.:26.119  
##                                        5                      :12      Max.   :44.441  
##                                        (Other)                :14

Table 4.3: Highest emitting LSOAs (per dwelling)
LSOA11CD	WD18NM	IMD_Decile_label	All_Tco2e_per_dw
E01023073	Totton Central	10 (10% least deprived)	44.4
E01023087	Totton South	8	37.0
E01023009	Dibden and Hythe East	9	35.9
E01023026	Forest North West	5	35.6
E01023047	Marchwood	10 (10% least deprived)	35.1
E01023048	Marchwood	8	35.0

Table 4.3: Lowest emitting LSOAs (per dwelling)
LSOA11CD	WD18NM	IMD_Decile_label	All_Tco2e_per_dw
E01023042	Lymington Town	6	8.5
E01023057	Milton	7	8.6
E01023070	Ringwood South	6	8.8
E01023029	Holbury and North Blackfield	2	10.2
E01023056	Milton	6	10.2
E01023058	Pennington	3	10.2

4.2.1.2 Home energy use

Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.

## Per dwelling T CO2e - gas emissions

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   55.29 2006.21 2332.00 2321.89 2695.73 3999.22

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -6.4062, df = 111, p-value = 3.713e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6426000 -0.3703594
## sample estimates:
##        cor 
## -0.5195439

Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.

## Per dwelling T CO2e - elec emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = 0.7354, df = 111, p-value = 0.4636
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1165976  0.2511315
## sample estimates:
##      cor 
## 0.069632

Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - elec emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = 0.7354, df = 111, p-value = 0.4636
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1165976  0.2511315
## sample estimates:
##      cor 
## 0.069632

Table 4.4: Summary of per dwelling energy emissions for LSOAs by urban/rural code
RUC11	mean_gas_kgco2e	mean_elec_kgco2e	mean_other_energy_kgco2e
Rural town and fringe	2,604.7	1,095.4	126.9
Rural village and dispersed	1,745.5	1,743.6	1,180.0
Urban city and town	2,343.9	1,040.1	90.9

Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 3.0604, df = 111, p-value = 0.002772
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.09933633 0.44095483
## sample estimates:
##       cor 
## 0.2789474

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.

Repeat for all home energy - includes estimates of emissions from oil etc

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 5.835, df = 111, p-value = 5.407e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3292252 0.6142446
## sample estimates:
##       cor 
## 0.4844895

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

How does the correlation look now?

4.2.1.3 Transport

We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)

Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - car emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -2.7076, df = 111, p-value = 0.00785
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.41457186 -0.06726717
## sample estimates:
##       cor 
## -0.248904

Table 4.5: Summary of per dwelling transport emissions for LSOAs by urban/rural code
RUC11	mean_car_kgco2e	mean_van_kgco2e
Rural town and fringe	3,249.5	357.1
Rural village and dispersed	4,073.6	721.0
Urban city and town	2,899.7	442.4

Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.

## Per dwelling T CO2e - van emissions

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = 1.7105, df = 111, p-value = 0.08997
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02522299  0.33506424
## sample estimates:
##       cor 
## 0.1602535

4.2.2 Estimating the annual emissions levy

Case studies:

Annual carbon tax
Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity

BEIS/ETC Carbon ‘price’

2020 - £21.93 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/849276/Carbon__Penalty_Price_Determination_for_2020.pdf

EU carbon ‘price’

September 2021: €60 (£51) https://tradingeconomics.com/eecxm:ind

BEIS Carbon ‘Value’ https://www.gov.uk/government/publications/valuing-greenhouse-gas-emissions-in-policy-appraisal/valuation-of-greenhouse-gas-emissions-for-policy-appraisal-and-evaluation#annex-1-carbon-values-in-2020-prices-per-tonne-of-co2

based on a Marginal Abatement Cost (MAC)
2021:
- Low: £122/T
- Central: £245/T <- use the central value for now
- High: £367/T

Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)

4.2.2.1 Scenario 1: Central cost

Table 4.6 below shows the overall £ GBP total for the case study area in £M under Scenario 1.

Table 4.6: Total £m GBP levy by source
nLSOAs	beis_GBPtotal_c	beis_total_c_gas	beis_GBPtotal_c_elec
113	404.7	46.1	22.2

Table 4.6: Total £m GBP levy by source, ordered by highest emitting region
region	nLSOAs	beis_GBPtotal_c	beis_total_c_gas	beis_GBPtotal_c_elec
South East	113	404.7	46.1	22.2

Figure 4.7: Proportion of total emissions due to gas & electricity use by region covered

The table below shows the mean per dwelling value rounded to the nearest £10.

(#tab:GBP_beis_c_perdw)Mean £ GBP levy per dwelling by source
All_emissions	Gas	Electricity	Gas + Electricity
5,231.0	568.9	275.3	844.2

Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.8: £k per LSOA revenue using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.9: £k per LSOA revenue using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2079    4013    5009    5231    6399   10888

Table 4.7: Highest levy LSOAs
LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_allEmissions_levy
E01023073	New Forest 003A	Totton Central	44.4	10,888.0
E01023087	New Forest 005E	Totton South	37.0	9,071.2
E01023009	New Forest 009B	Dibden and Hythe East	35.9	8,803.8
E01023026	New Forest 010A	Forest North West	35.6	8,712.7
E01023047	New Forest 008C	Marchwood	35.1	8,606.9
E01023048	New Forest 008D	Marchwood	35.0	8,567.1

Table 4.7: Lowest levy LSOAs
LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_allEmissions_levy
E01023042	New Forest 017D	Lymington Town	8.5	2,078.5
E01023057	New Forest 021E	Milton	8.6	2,112.0
E01023070	New Forest 012D	Ringwood South	8.8	2,147.6
E01023029	New Forest 013A	Holbury and North Blackfield	10.2	2,496.4
E01023056	New Forest 021D	Milton	10.2	2,497.2
E01023058	New Forest 019C	Pennington	10.2	2,500.9

Figure ?? repeats the analysis but just for gas.

Anything unusual?

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.11: £k per LSOA incurred via gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   13.55  491.52  571.34  568.86  660.45  979.81

Table 4.8: Highest emitting by £ GBP gas levy
LSOA11CD	LSOA01NM	WD18NM	gas_T_CO2e_pdw	GBP_gas_levy_perdw
E01022998	New Forest 015C	Brockenhurst and Forest South East	4.0	979.8
E01022997	New Forest 016D	Bransgore and Burley	3.7	894.6
E01022977	New Forest 006A	Ashurst, Copythorne South and Netley Marsh	3.4	842.9
E01022991	New Forest 015B	Boldre and Sway	3.3	805.7
E01023044	New Forest 023A	Lymington Town	3.3	801.3
E01023051	New Forest 023B	Milford	3.2	775.3

Table 4.8: Lowest emitting by £ GBP gas levy
LSOA11CD	LSOA01NM	WD18NM	gas_T_CO2e_pdw	GBP_gas_levy_perdw
E01023010	New Forest 008B	Dibden and Hythe East	1.6	381.2
E01023026	New Forest 010A	Forest North West	0.8	195.0
E01023012	New Forest 001B	Downlands and Forest	0.6	152.1
E01022993	New Forest 007B	Bramshaw, Copythorne North and Minstead	0.1	26.6
E01022999	New Forest 017B	Brockenhurst and Forest South East	0.1	16.7
E01023025	New Forest 001G	Forest North West	0.1	13.5

Figure ?? repeats the analysis for electricity.

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.13: £k per LSOA incurred via electricity using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   199.9   239.3   257.5   275.3   288.8   523.8

Table 4.9: Highest emitting by £ GBP electricity levy
LSOA11CD	LSOA01NM	WD18NM	elec_T_CO2e_pdw	GBP_elec_levy_perdw
E01023026	New Forest 010A	Forest North West	2.1	523.8
E01022993	New Forest 007B	Bramshaw, Copythorne North and Minstead	2.1	512.3
E01022999	New Forest 017B	Brockenhurst and Forest South East	2.0	482.3
E01023025	New Forest 001G	Forest North West	1.9	472.1
E01023012	New Forest 001B	Downlands and Forest	1.8	444.1
E01022998	New Forest 015C	Brockenhurst and Forest South East	1.7	404.9

Table 4.9: Lowest emitting by £ GBP electricity levy
LSOA11CD	LSOA01NM	WD18NM	elec_T_CO2e_pdw	GBP_elec_levy_perdw
E01023019	New Forest 020C	Fernhill	0.9	213.6
E01023010	New Forest 008B	Dibden and Hythe East	0.9	213.2
E01023057	New Forest 021E	Milton	0.9	212.9
E01023042	New Forest 017D	Lymington Town	0.8	207.9
E01023058	New Forest 019C	Pennington	0.8	202.3
E01023043	New Forest 017E	Lymington Town	0.8	199.9

Figure ?? shows the same analysis for measured energy (elec + gas)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.15: £k per LSOA incurred via electricity and gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   485.7   748.5   832.3   844.2   943.3  1384.8

4.2.2.2 Scenario 2: Rising block tariff

Applied to per dwelling values (not LSOA total) - may be methodologically dubious?

Cut at 25%, 50% - so any emissions over 50% get high carbon cost

## Cuts for total per dw

##        0%       25%       50%       75%      100% 
##  8483.753 16380.182 20446.677 26119.403 44440.945

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Table: (#tab:estimateAnnualLevyScenario2Total)Data summary

Name	…[]
Number of rows	113
Number of columns	3
Key	NULL
_______________________
Column type frequency:
numeric	3
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
V1	1	21.35	7.41	8.48	16.38	20.45	26.12	44.44	▅▇▅▂▁
beis_GBPtotal_sc2_perdw	1	3742.87	2261.88	1035.02	1998.38	2966.15	5076.56	11800.57	▇▃▂▁▁
beis_GBPtotal_sc2	1	2476261.07	1201986.26	757156.40	1537292.85	2180430.00	3090855.04	5994689.48	▇▇▅▂▁

Table 4.11: Compare totals for Scenario 1 & Scenario 2 (£m)
nLSOAs	sum_total_sc1	sum_total_sc2
113	404.7	279.8

4.2.3 Compare scenarios

Figure 4.16 compares the % £ levy under each scenario for all consumption contributed by LSOAs in each IMD decile.

Figure 4.16: Comparing £ levy under each scenario by IMD decile - all consumption emissions

Figure 4.17 compares the £ levy under each scenario for all consumption.

Figure 4.17: Comparing £ levy under each scenario - all consumption emissions

## [1] 29.53842

## [1] 14.91177

Table 4.12: Scenario 2 totals for: New Forest (£ m)
nLSOAs	sumAllConsEmissions_GBP	sumGasEmissions_GBP	sumElecEmissions_GBP
113	279.8	29.5	14.9

Table 4.12: Scenario 2 totals by region - if multiple regions (£ m)
region	nLSOAs	sumAllConsEmissions_GBP	sumGasEmissions_GBP	sumElecEmissions_GBP	sumPop
South East	113	279.8	29.5	14.9	178,210

Figure 4.18: Contribution to sum levy £ GBP by source

4.3 Estimate retofit costs

from A-E <- £13,300
from F-G <- £26,800

Source: English Housing Survey 2018 Energy Report

Model excludes EPC A, B & C (assumes no need to upgrade)

Adding these back in would increase the cost… obvs

4.3.1 Impute EPC counts

In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…

Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.

## N EPCs

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   153.0   289.0   340.0   371.4   413.0   943.0

## N elec meters

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   325.0   609.0   676.0   720.2   786.0  1311.0

Correlation between high % EPC F/G or A/B and deprivation?

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Now we need to convert the % to dwellings using the number of electricity meters (see above).

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.3.2 Estimate costs

Table 4.13 reports total retofit costs.

## To retrofit D-E (£m)

## [1] 634.9637

## Number of dwellings: 47742

## To retrofit F-G (£m)

## [1] 100.1882

## Number of dwellings: 3738

## To retrofit D-G (£m)

## [1] 735.1519

## To retrofit D-G (mean per dwelling)

## [1] 14174.64

Table 4.13: Retrofit cost totals (£m GBP): New Forest
meanPerLSOA_GBPm	total_GBPm
6.5	735.2

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.19 shows the LSOA level retofit costs per dwelling by IMD decile.

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.19: LSOA level retofit costs per dwelling by IMD score

4.4 Compare levy with costs

4.4.1 Scenario 1

Totals

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Repeat per dwelling

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.4.2 Scenario 2

Totals

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Repeat per dwelling

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

4.5 Years to pay…

4.5.1 Scenario 1

Figure 4.20 shows years to pay under Scenario 1 (all emissions)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.254   2.223   2.777   3.069   3.554   7.113

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.20: Years to pay under Scenario 1 (all em issions)

## Median years: 2.78

Figure 4.21 shows years to pay under Scenario 1 (energy emissions)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.80   14.85   16.97   17.43   18.59   37.54

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.21: Years to pay under Scenario 1 (energy emissions)

## Median years: 16.97

Figure 4.22 shows the year 1 outcome if levy is shared equally (all emissions levy).

Figure 4.22: Year 1 outcome if levy is shared equally (all emissions levy)

Table 4.14: Highest retofit sum cost under Scenario 1
LSOA11CD	LSOA11NM	WD18NM	retrofitSum	yearsToPay	epc_D_pc	epc_E_pc	epc_F_pc	epc_G_pc
E01022999	New Forest 017B	Brockenhurst and Forest South East	19,333,683.6	37.5	0.2	0.4	0.3	0.1
E01022991	New Forest 015B	Boldre and Sway	11,470,612.6	12.8	0.4	0.2	0.1	0.0
E01022983	New Forest 022C	Barton	10,491,560.0	18.8	0.6	0.2	0.0	0.0
E01023036	New Forest 018F	Hordle	10,356,599.3	14.9	0.5	0.2	0.0	0.0
E01022981	New Forest 022A	Barton	10,172,208.8	14.5	0.6	0.2	0.0	0.0
E01022993	New Forest 007B	Bramshaw, Copythorne North and Minstead	9,963,319.3	32.4	0.3	0.4	0.2	0.1
E01023012	New Forest 001B	Downlands and Forest	9,664,767.1	27.7	0.3	0.4	0.2	0.1
E01023025	New Forest 001G	Forest North West	9,131,605.5	34.4	0.3	0.3	0.2	0.1
E01022987	New Forest 022D	Becton	9,083,543.0	14.3	0.5	0.2	0.0	0.0
E01023022	New Forest 001D	Fordingbridge	9,007,972.0	21.3	0.3	0.2	0.0	0.0

Figure 4.23 shows the year 1 outcome if levy is shared equally (energy emissions levy).

Figure 4.23: Year 1 outcome if levy is shared equally (energy emissions levy)

What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…

4.5.2 Scenario 2

Figure 4.24 shows years to pay under Scenario 2 (all emissions)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.157   2.814   4.627   5.280   7.137  14.284

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.24: Years to pay under Scenario 2 (all em issions)

## Median years: 4.63

Figure 4.25 shows years to pay under Scenario 2 (energy emissions)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.80   14.85   16.97   17.43   18.59   37.54

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Figure 4.25: Years to pay under Scenario 2 (energy emissions)

Figure 4.26 shows the year 1 outcome if levy is shared equally (all emissions levy).

Figure 4.26: Year 1 outcome if levy is shared equally (all emissions levy)

Figure 4.27 shows the year 1 outcome if levy is shared equally (energy emissions levy).

Figure 4.27: Year 1 outcome if levy is shared equally (energy emissions levy)

What happens in Year 2 totally depends on the rate of upgrades…

4.5.2.1 Compare scenarios

Figure 4.28 compares pay-back times for the two scenarios - who does the rising block tariff help?

Figure 4.28: Comparing pay-back times across scenarios

5 R environment

5.1 R packages used

base R (R Core Team 2016)
bookdown (Xie 2016a)
data.table (Dowle et al. 2015)
ggplot2 (Wickham 2009)
kableExtra (Zhu 2018)
knitr (Xie 2016b)
rmarkdown (Allaire et al. 2018)
skimr (Arino de la Rubia et al. 2017)

5.2 Session info

6 Data Tables

I don’t know if this will work…

## Doesn't

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.

Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.

Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.

R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.

———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.

Zhu, Hao. 2018. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.

Data notebook: Simulating a local emissions levy to fund local energy effiency retrofit

Focus on: New Forest

Ben Anderson

Last run at: 2021-10-21 13:03:24